Some like it tough: Improving model generalization via progressively increasing the training difficulty
This incremental improvement addresses generalization issues in neural network training for image classification.
The paper tackles the problem of improving neural network generalization by introducing mini-batch trimming, a strategy that progressively increases training difficulty by focusing on high-loss samples in later stages, resulting in reduced test error on image classification tasks.
In this work, we propose to progressively increase the training difficulty during learning a neural network model via a novel strategy which we call mini-batch trimming. This strategy makes sure that the optimizer puts its focus in the later training stages on the more difficult samples, which we identify as the ones with the highest loss in the current mini-batch. The strategy is very easy to integrate into an existing training pipeline and does not necessitate a change of the network model. Experiments on several image classification problems show that mini-batch trimming is able to increase the generalization ability (measured via final test error) of the trained model.